Research on Joint Extraction Method of Entity and Relation Triples Based on Hierarchical Cascade Labeling

As an important research field of artificial intelligence, knowledge graph develops rapidly, and triplet extraction is the key to the construction of a knowledge graph. The traditional pipeline extraction method will bring the error of entity recognition into the relationship extraction and affects the extraction effect. Besides, the traditional pipeline extraction method cannot solve the SEO (Single Entity Overlap) and EPO (Entity Pair Overlap) problems. Inspired by this, we compare the advantages and disadvantages of the mainstream methods of entity and relationship triples joint extraction, propose a new joint extraction method of entity relation triples based on a hierarchical cascade labeling model (named HCL model), and the HCL model is based on multi neural network cooperation. Further, we construct a balanced sampling Chinese dataset about the entity and relational triplet extraction which contains SEO and EPO. We carry out the experiments on the balanced data set, and the F1 value of the HCL model reaches 65.4% better than other baseline models.